Data for "Assessing the contribution of extratropical cyclones to river floods that caused property damage in Quebec, Canada"
Bibliographic record
Abstract
In the past few decades, the province of Quebec in eastern Canada has experienced widespread and costly flood events, yet the contribution of extratropical cyclones to these floods remains unquantified. This dataset was created for the "Assessing the contribution of extratropical cyclones to river floods that caused property damage in Quebec, Canada" publication (in review) and aims to highlight the common characteristics of extratropical cyclones that have contributed to flooding in the province between January 1991 and December 2020. Using reconstituted river output data, watersheds, and governmental financial aid data, a 498-events local_floods dataset was created (on the watershed level). From these local floods, a 85-events regional_floods dataset was created, grouping local floods happening around the same time, but in different watersheds. A large domain, named Baseline Southern Quebec, was created to identify relevant extratropical cyclones to the study area and period. From this domain, 6,227 relevant Baseline Southern Quebec extratropical cyclones (which spent at least one hour inside the domain during the study period) were extracted from the NAEC Catalogue (Chen et al., 2022b). We created two datasets with information related to these Baseline Southern Quebec storms: one containing their entire lifetime tracks (NAEC_9120) and one with only their tracks while they were inside the Baseline Southern Quebec domain (NAEC_9120_BSQ). Among these Baseline Southern Quebec extratropical cyclones, 200 were identified as contributing to flood events (by being associated with high percentages of rainfall during flood events). These were labelled flood ETCs, and are presented in the flood_ETCs dataset. The rest of the identified Baseline Southern Quebec storms (N = 6,027) were labelled as non-flood ETCs. Alternative scenarios from the control case were studied for spring (MAM) floods (N = 372). These other scenarios involve reducing the search radius for relevant storms (radius), reducing the time interval studied surrounding the flood events (days), and increasing the necessary threshold for being labelled as a flood ETC (threshold). For each of these alternative scenarios, flood_ETCs and local_floods datasets were produced.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".